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      Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China

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          Abstract

          Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

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          Most cited references38

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          Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018)

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            The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

            Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial–temporal filtering and logical reasoning to further improve the spatial–temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).
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              Geoprobes: Principles and prospects

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                Author and article information

                Contributors
                jinlong_cheng@126.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 June 2024
                7 June 2024
                2024
                : 14
                : 13167
                Affiliations
                [1 ]College of Geography and Tourism, Luoyang Normal University, ( https://ror.org/029man787) Luoyang, 471022 China
                [2 ]College of Law and Sociology, Luoyang Normal University, ( https://ror.org/029man787) Luoyang, 471022 China
                [3 ]College of Geography and Environmental Science, Henan University, ( https://ror.org/003xyzq10) Kaifeng, 475004 China
                [4 ]Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, ( https://ror.org/003xyzq10) Kaifeng, 475004 China
                Article
                64164
                10.1038/s41598-024-64164-8
                11161464
                38849513
                d320758e-ed96-498f-ae85-5f5a5d65bb55
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 March 2024
                : 5 June 2024
                Funding
                Funded by: The National Natural Science Foundation of China
                Award ID: 42101206
                Award Recipient :
                Funded by: the Key Research Project of Higher Education Institutions of Henan Province
                Award ID: 24A170023, 24A170022, 24B170008
                Award ID: 24A170023, 24A170022, 24B170008
                Award ID: 24A170023, 24A170022, 24B170008
                Award Recipient :
                Funded by: Outstanding Youth Science Fund of Henan Province
                Award ID: 24HASTIT050
                Award Recipient :
                Funded by: Henan Science and Technology Innovation Talent Project
                Award ID: 41901588
                Award Recipient :
                Funded by: the Key R&D and Promotion Projects in Henan Province_key projects of soft science research
                Award ID: 232400411024
                Award Recipient :
                Categories
                Article
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                © Springer Nature Limited 2024

                Uncategorized
                green urbanization,tourism competitiveness,spatial coupling relation,interactive mechanism,coordinated development strategies,environmental sciences,environmental social sciences

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